Student Projects

Integrating genomic data to characterise inherited risk factors for mental health disorders

Project Supervisor/s

This project is suitable for PhD or Honours. We are seeking a highly motivated student with a strong interest in statistics and quantitative studies.


Mental health disorders, including depression, anxiety, and substance abuse disorders, afflict around half of the individuals at some point in their lives and account for a substantial proportion of the global burden of disease. Recently, significant progress has been made in identifying genetic (i.e., inherited) risk factors associated with mental health disorders through genome-wide association (GWA) studies of large, population-based cohorts.

Although these GWA studies have implicated many genetic risk factors for mental health disorders, identifying the exact causal genes remains challenging. This is due in part to complex interactions between multiple cellular data types in specific tissues that are likely to mediate susceptibility. Integrated studies of multiple cellular data, such as DNA sequence variation, gene expression, and DNA methylation, in relevant tissues are therefore required to understand the impact of genetic risk factors on mental health.

This project will use high-quality gene expression and DNA methylation data measured in whole blood to characterise genetic risk factors underlying mental health disorders. Analyses will then be conducted across tissues using several publicly available multi-tissue genomic compendia. This study will provide a unique resource to identify and characterise novel genetic factors underlying susceptibility to mental health disorders. The identification of such causal genes is the next crucial step in elucidating the complex molecular pathways of mental health disorders and may help in the development of diagnostic tests and more rational treatment strategies.


  • To characterise genetic risk factors for psychiatric disorders in a large population-based sample.
  • To prioritise causal tissues and mechanisms using independent multi-tissue genomic compendia.


  • A position in a dynamic research environment and the opportunity to conduct high-quality studies.
  • Access to large-scaled datasets through (inter)national collaborations.

To apply for this project, please contact the project supervisor/s

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